Current data warehouse and OLAP technologies can be applied to analyze the structured data that organisations store in their databases. These organisations also produce many documents and use the web as their largest source of external information. Examples of internal and external sources of information include e.g. reports on purchase trends and market-research.
The term OLAP designates a category of databases, applications and technologies that allow the collection, storage, manipulation and reproduction of multidimensional data, with the goal of data analysis. Typically, OLAP databases comprise one—or at least fewer—tables than a comparable relational database. OLAP databases typically store pre-computed aggregations of data to make aggregated figures readily available for data analysis purposes.
Databases for analysis purposes and their user interfaces are more and more frequently used by people with no specialist knowledge of databases as a powerful tool to present and analyse facts collected within their sphere of profession. These people take to the databases driven by a desire to be able to more efficiently gain insight into the facts relevant to their profession.
However, there seems to be an ever lasting demand for increased performance of such database tools and a demand for improved efficiency in use of such tools. A simple, but highly relevant measure of efficiency in use of such tools is the number of user interactions required to arrive at a desired result. Another measure is the complexity of the interactions. If the user, who may not be a database expert, experiences that the complexity of the userinteractions is relatively high and also is somehow beside his expectations of what is required to arrive at a desired result, the user tends to get annoyed or frustrated and may give up using the database tool.